The present invention is directed to an infrared defect inspection system and, more particularly, high speed defect detection utilizing near and mid-infrared imaging, high speed image processing, comparison and contrast, processed image evaluation and characterization, and the development of control signals for sorting or separating objects or items based on the defect determination. More particularly, the present invention relates to methods of near and mid-infrared imaging for defect inspection and fruit stem-end and calyx identification.
U.S. Pat. Nos. 5,339,963 and 5,533,628 each issued to Yang Tao and assigned to Agri-Tech, Incorporated, are each hereby incorporated by reference, and described methods and apparatus for sorting objects by color, and in particular are directed to the sorting of apples. The color sorting apparatus has a singulator section, a color sorter, and a conveyor which drops the sorted objects into appropriate collection bins. The objects for sorting are transported on an endless conveyor through the singulation and color sorting section. An independently adjustable speed belt rotates in the same direction as the wheels and operates to provide a view of each of the four sides of the object to an imaging device such as a camera which supplies red, green and blue signals to an image processor which performs a color transformation and obtains a single composite hue value for each object or piece of fruit to be sorted. Based on a comparison of the hue value to the user program grading criteria, signals are provided to the conveyor so that the objects are ultimately deposited in the appropriate sorting bins.
Allowed U.S. patent application Ser. No. 08/483,962 filed Jun. 7, 1995, and U.S. Pat. No. 5,732,147 on Mar. 24, 1998 is hereby incorporated by reference and describes an image processing system using cameras and image processing techniques to identify undesirable objects on roller conveyor lines. The cameras above the conveyor capture images of the passing objects (such as apples). The roller background information is removed and images of the objects remain. To analyze each individual object accurately, the adjacent objects are isolated and small noisy residue fragments are removed. A spherical optical transformation and a defect preservation transformation preserve any defect levels on objects even below the roller background and compensate for the non-lambertian gradient reflectants on spherical objects at their curvatures and dimensions. Defect segments are then extracted from the resulting transformed images. The size, level and pattern of the defect segments indicate the degree of defects in the object. The extracted features are fed into a recognition process in a decision-making system for grade rejection decisions. The locations and coordinates of the defects generated by defect allocation function are combined with defect rejection decisions and user parameters to signal appropriate mechanical actions such as to separate objects with defects from those that do not contain defects.
Conventional attempts at using laser scanning and reflectance to detect line shifts or changes in height of the object in order to attempt to detect defects in fruit or other objects have not been successful and are not accurate due to the inability to provide the same orientation of each object, changes in size and shape between individual pieces or items of fruit, and the like.
Still further, it has been difficult to differentiate between true defects such as bruises, limb rub, bulls-eyes, fungus such as black net, blemishes, cuts, injuries, stem punches, cracks, worm holes, insect damage, disease damage, color defects, Russet and the like from the fruit stem-end, stem, calyx, or blossom. Hence, there is a need for an improved method and apparatus for defect detection, apple defect detection as compared to detection of the stem-end, stem, and/or calyx identification, defects in smooth surfaces, and/or defect detection and object or item sorting or separation based thereon.